Browserai MCP

Empower AI Agents with Real-Time Web Data

🌟 Overview

Welcome to the Browserai Model Context Protocol (MCP) server, designed to enable LLMs, AI agents, and applications to access, discover, and extract web data in real-time. This server empowers MCP clients—such as Claude Desktop, VS Code, Cursor, and WindSurf—to seamlessly search the web, navigate websites, perform actions, and retrieve data efficiently, even from sites with anti-scraping measures.

⚙️ How it Works

The Browserai MCP server functions as an intermediary between your AI agent (the MCP client) and the internet:

1. Your AI agent (e.g., Claude Desktop, a VSCode extension) dispatches a request to the Browserai MCP server via the Model Context Protocol.
2. The MCP server, utilizing your Browserai API token and project configurations, executes the requested web action (e.g., search, navigate, extract data).
3. It leverages Browserai's robust infrastructure to manage complexities such as bypassing geo-restrictions and bot detection mechanisms.
4. The server then delivers the structured data or action outcome back to your AI agent.

This architecture allows your agent to access real-time web information and capabilities without the need to directly manage browser instances or anti-blocking technologies.

✨ Features

• Real-Time Web Access: Retrieve up-to-date information directly from the web.
• Bypass Geo-restrictions: Access content without geographical limitations.
• Web Unlocker: Navigate websites protected by bot detection systems.
• Browser Control: Utilize optional remote browser automation capabilities.
• Seamless Integration: Compatible with all MCP-compliant AI assistants.

🔧 Account Setup

To begin using the Browserai MCP server, a Browserai account and API key are required.

1. Ensure you have an account on browser.ai. New users receive free credits for testing, with pay-as-you-go options available.
2. Obtain your API key from the user dashboard.
3. Create a new project in your dashboard.
   • This project name can be overridden in your MCP server configuration using the PROJECT_NAME environment variable.

🚀 Quickstart

This guide assists in setting up the Browserai MCP server with common AI clients.

1. Install Node.js: The npx command is required. If Node.js is not already installed, download and install it from the node.js website. npx is a Node.js package runner that simplifies the execution of CLI tools like @brightdata/browserai-mcp.

Claude Desktop

1. Navigate to Claude > Settings > Developer > Edit Config > claude_desktop_config.json and add the following configuration:

{
  "mcpServers": {
    "Browserai": {
      "command": "npx",
      "args": ["@brightdata/browserai-mcp"],
      "env": {
        "API_TOKEN": "<your-browserai-api-token>",
        "PROJECT_NAME": "<your-browserai-project-name (optional)>"
      }
    }
  }
}

VSCode Agent

1. Configure your VSCode Agent. This usually involves modifying a settings file. For instance, create a .vscode/mcp.json file in your project with the following content:

{
  "servers": {
    "browserai-mcp": {
      "type": "stdio",
      "command": "npx",
      "args": ["@brightdata/browserai-mcp"],
      "env": {
        "API_TOKEN": "<your-browserai-api-token>",
        "PROJECT_NAME": "<your-browserai-project-name (optional)>"
      }
    }
  }
}

Note for VSCode Agent: The specific path and structure for the MCP server configuration (e.g., the filename .vscode/mcp.json or the JSON key like "servers") may differ based on the VSCode Agent extension in use. Consult your VSCode Agent's documentation for precise

... View full README on GitHub